US6859228B1 - Least squares method for color misregistration detection and correction in image data - Google Patents
Least squares method for color misregistration detection and correction in image data Download PDFInfo
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- US6859228B1 US6859228B1 US09/419,602 US41960299A US6859228B1 US 6859228 B1 US6859228 B1 US 6859228B1 US 41960299 A US41960299 A US 41960299A US 6859228 B1 US6859228 B1 US 6859228B1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/58—Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/10—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths
- H04N23/13—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths with multiple sensors
- H04N23/15—Image signal generation with circuitry for avoiding or correcting image misregistration
Definitions
- This invention relates to methods of image capture, more particularly for methods of detecting color misregistration in image capture devices.
- Color image capture devices typically operate by capturing primary color component signals such as red, green and blue (RGB) from a set of charge coupled devices (CCDs).
- the CCDs are normally arranged in the main scan direction.
- the sub-scan direction the direction in which the scanning bar moves, will be referred to as the Y direction and the main scan direction, perpendicular to the sub-scan direction, will be referred to as X.
- CCDs capture the image in one pass or in three passes, one for each primary color component. Regardless of the number of passes, however, there is typically some misalignment in the RGB signals. This misalignment between colors is referred to as color misregistration. It is caused by faulty superposition of the three colors. It normally manifests itself as color fringes on the edges of the objects that were scanned, either text, graphics or drawings.
- Color fringes normally appear as either cyan or magenta fringes on the edges of the scanned objects. Cyan fringes result from misregistration of the red signal, and magenta fringes result from misregistration of the green signal.
- the human eye does not normally detect misregistration of the blue signal, because of its low bandwidth and low contrast sensitivity.
- a fiber optic detection means is used to detect registration signals produced by a retroreflector.
- the light from the retroreflector is analyzed and used to adjust the registration of the belt.
- One aspect of the invention is a method for detecting color misregistration in image data.
- Input image data is buffered as color space data.
- the color space data is then transferred to vector space.
- An examination window around a current pixel is established. Background and foreground pixels in this window are determined.
- the current pixel is examined to determine if it is on an edge of a scanned object, either text, image or graphic. If the pixel meets all these requirements it is deemed to have color misregistration.
- a correction value is then determined and adjusted before being applied to the pixel value.
- the correction value can be determined using normal least squares. It can be adjusted by the application of fuzzy logic.
- FIG. 1 shows a flowchart of one embodiment of a method for detecting color misregistration in accordance with the invention.
- FIG. 2 shows a schematic representation of a pixel layout in the sub-scan direction, in accordance with the invention.
- RGB cyan-magenta-yellow
- CMYK cyan-magenta-yellow-black
- FIG. 1 shows a flow chart of one embodiment of a process for detection of color misregistration in accordance with the invention.
- the input data is received from the color image capture device, typically RGB data.
- the data is digitized and buffered. For purposes of this discussion, the data is assumed to be digitized at eight bits per color.
- This data is then processed in RGB vector space in a color misregistration detection circuitry or process.
- the invention could be implemented as software or in hardware.
- step 12 the line selected in step 10 is then transferred to vector space.
- the vector space uses two color pixels, which will be referred to as pixel A and pixel B.
- step 14 an examination area or window of interest must be established as shown in step 14 .
- the variations of size and direction of this window is left to the designer. For purposes of the discussion only, a window of 5 pixels by 1 pixel will be assumed. A schematic representation of this type of window is shown in FIG. 2 .
- the pixel of interest is pixel 0.
- Two pixels on either side, before ( ⁇ ) and after (+) the pixel of interest are used in the analysis. In this example, these 5 pixels are in the sub-scan, or Y, direction. Only one pixel width is used in the scan direction. As has been mentioned, the dimensions of the window are left up to the designer.
- step 16 of the process shown in FIG. 1 the pixel is analyzed to determine whether or not it is on an edge.
- Edge detection may be performed in many ways. For example, a Sobel filter or a gradient filter may be used.
- a special gradient edge detector can also be used. Using the window established in step 16 , gradients between the pixel of interest and its neighbors are determined. If the gradients fall below a predetermined threshold, no edge is detected. Since color misregistration typically occurs at the edges of scanned objects, such as text, drawings or images, pixels not on an edge are not considered to be candidates for color misregistration. If the result of edge detection at step 16 is negative, the process continues to step 28 and ends with respect to that pixel.
- step 16 performs only an initial determination of edge detection. A much more detailed analysis is performed further in the process. Step 16 is an optional step, which can speed the process by further narrowing the pixels upon which more advanced computations must be performed.
- step 16 If the result of edge detection in step 16 is positive, the process moves on to step 18 to differentiate between foreground pixels and background pixels. Again, there are several options for this determination. However, for this discussion, one of two approaches will be discussed. The pixels within the window are analyzed to determine darkest or lightest pixels. Alternatively, the pixels could be compared against a predetermined pattern. Further in the process, the current pixel will be analyzed in comparison to the foreground and background, so the identification of these components of the image is important.
- scanned objects can include text characters, drawings or images.
- edges of any of these objects are candidates for color misregistration.
- step 20 of the process a two-step method will be used for step 20 of the process.
- the first step will be to check the gradient of the pixel of interest. To be in the edge of an object, the gradient between the foreground and background must be higher than the gradients between the current pixel and the background, and the current pixel and the foreground.
- Pixel 0 is the designation of the current pixel under study.
- D the magnitude of the gradients, and a and b for foreground and background: D ( a,b )> D ( a, 0); and D ( a,b )> D ( b, 0).
- a luminance check may also be performed. Some approximation is used to convert the foreground (a), background (b), and current pixel (0) to luminance values.
- L ( a ) 0.5 G ( a )+0.3 R ( a )+0.2 B ( a ).
- step 22 If the results of this step are positive, and the pixel is in the edge of a scanned object, then the process continues on to step 22 . If the results are negative, this pixel is eliminated as a candidate for color misregistration. The process will continue to step 28 and ends with regard to this pixel.
- linear interpolation and normal least square projection is used to find the optimum correction point.
- Linear interpolation is accomplished by linearly connecting the foreground pixel P a and the background pixel P b discussed with reference to step 18 .
- the purpose of using least square projection is to find a point in the interpolation line that contains minimum energy to the current pixel P 0 .
- the normal least square projection can be represented by: Minimize(Distance( P 0 ⁇ Line( a,b )) 2 .
- This embodiment uses the normal least square approach, which is different from the traditional least square.
- the projection direction is perpendicular to the object and is independent of the coordinate system used.
- the normal least square projection is independent of the rotation in the coordinate system or the rotation of the object. It is also independent of the pixel values used in the current projection and interpolation for P 0 , P a , or P b .
- the above equation can be solved in several different ways, including analytical geometry, vector calculus, linear algebra, or other optimization techniques.
- This equation is then substituted in to the normal least squares equation above, which will find the value t corresponding to the projection point in the least squares approach. This value can then be used as the correction value for each color fringing pixel.
- step 22 the value is adjusted using fuzzy logic in step 24 .
- Fuzzy logic is a methodology developed by Professor Lotfi A. Zadeh at the University of California at Berkeley in 1965. It is used generally to describe a tool that allows intermediate values to be defined inside the range of conventional evaluations such as ON/OFF, YES/NO. The approach allows these intermediate values to be formulated mathematically and then processed by computers.
- fuzzy logic results in varying the degree of correction applied to a pixel with color misregistration, beyond the current approach in the art of either correcting or not correcting a particular pixel.
- the fuzzy logic could be applied in the following manner.
- the amount of correction is high. If the contrast between the foreground and background is low, then the amount of correction is low. This determination could be made using the most simple linear fuzzy approximation, or triangular function. The complexity of the fuzzy logic applied is only constrained by the selections of the system design and the system operating conditions.
- a second fuzzy objective could then take into account how closely a current pixel P 0 is to either the foreground or background. If it is close enough and indicates a borderline condition, the amount of correction is reduced. This could be implemented by using a step function to cut the correction factor in half in locations by the borderline. The exact designation of what constitutes a borderline condition can be adjusted to fit a particular system or application during implementation.
- the above process is implemented in software in the image capture device. It is possible that it could be implemented in the image output device that receives the image data from the image capture device. It could also be implemented in either part of a device that performs both image capture and image output. This process could be implemented in image or graphic application software, Raster Image Processors (RIP), or printer, copier or output device drivers, among others.
- RIP Raster Image Processors
- printer copier or output device drivers
- the process could be implemented in application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or in digital signal processors (DSP).
- ASIC application specific integrated circuits
- FPGA field programmable gate arrays
- DSP digital signal processors
- this process could be applied to color spaces other than RGB. It could be implemented in CMY, CMYK and chrominance and luminance based color spaces, such as LAB, LCH, HLS, etc. None of the above specifics or examples are intended to limit applicability of the invention.
Abstract
Description
P A=(R a , G a , B a); and P B=(R b , G b , B b).
The gradient between the two pixels to be
d ab=(d RAB , d GAB , d BAB), and its magnitude is D AB=magnitude(d AB).
D(a,b)>D(a,0); and D(a,b)>D(b,0).
L(a)=0.5G(a)+0.3R(a)+0.2B(a).
To be in the edge of an object, the luminance of the current pixel must be between the foreground and background luminance values.
L(b)<L(0)<L(a); or, L(a)<L(0)<L(b).
If the results of this step are positive, and the pixel is in the edge of a scanned object, then the process continues on to step 22. If the results are negative, this pixel is eliminated as a candidate for color misregistration. The process will continue to step 28 and ends with regard to this pixel.
Line(a,b): (R−R a)/(R b−Ra)=(G−G a)/(G b −G a)=(B−B a)/(B b −B a).
It must be noted that many other types of interpolation exist and are equally applicable. Some may result in equal or better performance in different circumstances.
Minimize(Distance(P 0−Line(a,b))2.
This embodiment uses the normal least square approach, which is different from the traditional least square. In normal least square, the projection direction is perpendicular to the object and is independent of the coordinate system used. The normal least square projection is independent of the rotation in the coordinate system or the rotation of the object. It is also independent of the pixel values used in the current projection and interpolation for P0, Pa, or Pb.
Line(t)=(P b −P a)t+P a.
This equation is then substituted in to the normal least squares equation above, which will find the value t corresponding to the projection point in the least squares approach. This value can then be used as the correction value for each color fringing pixel.
Claims (9)
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